y = np.concatenate((binding_sites_labels, other_labels)) # %% visualizer = ClassBalance(labels=class_names) visualizer.fit(y) visualizer.poof() # %% visualizer = ParallelCoordinates() visualizer.fit_transform(X, y) visualizer.poof() # %% visualizer = Rank1D() visualizer.fit(X, y) visualizer.transform(X) visualizer.poof() # %% visualizer = Rank2D() visualizer.fit_transform(X) visualizer.poof() # %% visualizer = FeatureCorrelation() visualizer.fit(X, y) visualizer.poof() # %% visualizer = FeatureCorrelation(method='mutual_info-classification') visualizer.fit(X, y)
X["timerecurrence"].describe() #%% # for column in X.columns[2:16]: # plt.scatter(X[column], y) # plt.xlabel(column) # plt.show() #%% from yellowbrick.features.radviz import RadViz features = X.columns[:13] visualizer = RadViz(classes=class_labels, features=features) visualizer.fit(X[features], y) visualizer.transform(df[features]) visualizer.show() #%% from yellowbrick.target import FeatureCorrelation visualizer = FeatureCorrelation(labels=features) visualizer.fit(X[features], y) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure #%% from yellowbrick.features import JointPlotVisualizer visualizer = JointPlotVisualizer()